{"id":"W1992214577","doi":"10.1109/cvprw.2012.6238911","title":"Shape matching of repeatable interest segments in 3D point clouds","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Robustness (evolution); Computer vision; Point cloud; Computer science; Segmentation; Matching (statistics); Pattern recognition (psychology); Point of interest; Object (grammar); Metric (unit); Image segmentation; Line segment; Cognitive neuroscience of visual object recognition; Point (geometry); Boundary (topology); Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007866732,0.0005071867,0.0009185195,0.002098698,0.0004804653,0.001251051,0.001787773,0.001148037,0.0009666369],"category_scores_gemma":[0.002928762,0.0006617404,0.000916901,0.001922603,0.0006931082,0.001625613,0.001386593,0.0007696848,0.0008678494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005540377,"about_ca_system_score_gemma":0.0005441121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002676904,"about_ca_topic_score_gemma":0.003018159,"domain_scores_codex":[0.9985163,0.0001859963,0.00006855355,0.0003092682,0.0007942721,0.0001256248],"domain_scores_gemma":[0.9983242,0.0003574403,0.0002548996,0.0006809378,0.0003194137,0.000063112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004764448,0.0001679753,0.005444804,0.0001599373,0.0001448602,0.0004614946,0.000511734,0.1199214,0.2901285,0.005300391,0.001116984,0.5761655],"study_design_scores_gemma":[0.00001787924,0.0001766352,0.00567488,0.00001662127,0.00002526618,0.0006189441,0.0001557412,0.8669813,0.1187405,0.004256373,0.003285482,0.00005035126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07219458,0.0001042202,0.9253253,0.00003661367,0.00001341584,0.00008558218,0.00009435126,0.00156583,0.0005800378],"genre_scores_gemma":[0.3722944,0.0001196753,0.6259159,0.00003561093,0.00001618542,0.0000736669,0.0005064241,0.0003061877,0.0007320343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002676904,"threshold_uncertainty_score":0.005322635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109871483911101,"score_gpt":0.2299385545824967,"score_spread":0.2088398397433857,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}